Deep transfer learning approaches in performance analysis of brain tumor classification using MRI images
Brain tumor classification is a very important and the most prominent step for assessing life‐
threatening abnormal tissues and providing an efficient treatment in patient recovery. To …
threatening abnormal tissues and providing an efficient treatment in patient recovery. To …
Customized VGG19 architecture for pneumonia detection in chest X-rays
Pneumonia is one of the major illnesses in children and aged humans due to the Infection in
the lungs. Early analysis of pneumonia is necessary to prepare for a possible treatment …
the lungs. Early analysis of pneumonia is necessary to prepare for a possible treatment …
Deep learning-powered biomedical photoacoustic imaging
Photoacoustic Imaging (PAI) is an emerging hybrid imaging modality that combines optical
imaging and ultrasound imaging, offering advantages such as high resolution, strong …
imaging and ultrasound imaging, offering advantages such as high resolution, strong …
Classification and analysis of pistachio species with pre-trained deep learning models
Pistachio is a shelled fruit from the anacardiaceae family. The homeland of pistachio is the
Middle East. The Kirmizi pistachios and Siirt pistachios are the major types grown and …
Middle East. The Kirmizi pistachios and Siirt pistachios are the major types grown and …
VGG19 network assisted joint segmentation and classification of lung nodules in CT images
Pulmonary nodule is one of the lung diseases and its early diagnosis and treatment are
essential to cure the patient. This paper introduces a deep learning framework to support the …
essential to cure the patient. This paper introduces a deep learning framework to support the …
Weighted average ensemble deep learning model for stratification of brain tumor in MRI images
Brain tumor diagnosis at an early stage can improve the chances of successful treatment
and better patient outcomes. In the biomedical industry, non-invasive diagnostic procedures …
and better patient outcomes. In the biomedical industry, non-invasive diagnostic procedures …
Automated brain tumor identification using magnetic resonance imaging: A systematic review and meta-analysis
O Kouli, A Hassane, D Badran, T Kouli… - Neuro-oncology …, 2022 - academic.oup.com
Background Automated brain tumor identification facilitates diagnosis and treatment
planning. We evaluate the performance of traditional machine learning (TML) and deep …
planning. We evaluate the performance of traditional machine learning (TML) and deep …
3D shearlet-based descriptors combined with deep features for the classification of Alzheimer's disease based on MRI data
Alzheimer's disease (AD) is a neurodegenerative disease that afflicts millions of people
worldwide. Early detection of AD is critical, as drug trials show a promising advantage to …
worldwide. Early detection of AD is critical, as drug trials show a promising advantage to …
Epileptic seizures detection in EEG signals using fusion handcrafted and deep learning features
Epilepsy is a brain disorder disease that affects people's quality of life.
Electroencephalography (EEG) signals are used to diagnose epileptic seizures. This paper …
Electroencephalography (EEG) signals are used to diagnose epileptic seizures. This paper …
Automated segmentation of leukocyte from hematological images—a study using various CNN schemes
Medical images play a fundamental role in disease screening, and automated evaluation of
these images is widely preferred in hospitals. Recently, Convolutional Neural Network …
these images is widely preferred in hospitals. Recently, Convolutional Neural Network …